A Federated Multi-Task Learning Framework with Dual Attention Mechanisms for Smart Buildings

Accurate energy forecasting and occupancy detection are critical for energy management and occupant comfort in smart buildings. Centralized models, for example, based on LSTM and GRU, perform well with homogeneous datasets but face challenges in distributed IoT settings due to privacy concerns and sensor data heterogeneity (e.g., light, temperature, humidity, $\text{CO}_{2}$). In this paper, we propose Federated Forecasting (FedFor), a privacy-preserving framework integrating Federated Learning (FL), Multi-Task Learning (MTL), and dual attention mechanisms. FedFor simultaneously enhances energy forecasting and occupancy detection by leveraging task-specific and temporal patterns while keeping data localized. We evaluated our approach on two datasets, namely ThingSpeak and Occupancy Detection Data (ODD). Results show that FedFor outperforms the baselines, achieving 99.11% accuracy for occupancy detection and reducing Mean Absolute Error (MAE) to 0.0097 for forecasting. Compared to state-of-the-art, our FedFor method is effective in addressing privacy concerns and data heterogeneity.